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AliZafar120/CannyEdgeDetector.java

Created October 24, 2017 12:58
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  1. AliZafar120 created this gist Oct 24, 2017.
    565 changes: 565 additions & 0 deletions CannyEdgeDetector.java
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    package Lab9;
    import java.awt.image.BufferedImage;
    import java.util.Arrays;

    /**
    * <p><em>This software has been released into the public domain.
    * <strong>Please read the notes in this source file for additional information.
    * </strong></em></p>
    *
    * <p>This class provides a configurable implementation of the Canny edge
    * detection algorithm. This classic algorithm has a number of shortcomings,
    * but remains an effective tool in many scenarios. <em>This class is designed
    * for single threaded use only.</em></p>
    *
    * <p>Sample usage:</p>
    *
    * <pre><code>
    * //create the detector
    * CannyEdgeDetector detector = new CannyEdgeDetector();
    * //adjust its parameters as desired
    * detector.setLowThreshold(0.5f);
    * detector.setHighThreshold(1f);
    * //apply it to an image
    * detector.setSourceImage(frame);
    * detector.process();
    * BufferedImage edges = detector.getEdgesImage();
    * </code></pre>
    *
    * <p>For a more complete understanding of this edge detector's parameters
    * consult an explanation of the algorithm.</p>
    *
    * @author Tom Gibara
    *
    */

    public class CannyEdgeDetector {

    // statics

    private final static float GAUSSIAN_CUT_OFF = 0.005f;
    private final static float MAGNITUDE_SCALE = 100F;
    private final static float MAGNITUDE_LIMIT = 1000F;
    private final static int MAGNITUDE_MAX = (int) (MAGNITUDE_SCALE * MAGNITUDE_LIMIT);

    // fields

    private int height;
    private int width;
    private int picsize;
    private int[] data;
    private int[] magnitude;
    private BufferedImage sourceImage;
    private BufferedImage edgesImage;

    private float gaussianKernelRadius;
    private float lowThreshold;
    private float highThreshold;
    private int gaussianKernelWidth;
    private boolean contrastNormalized;

    private float[] xConv;
    private float[] yConv;
    private float[] xGradient;
    private float[] yGradient;

    // constructors

    /**
    * Constructs a new detector with default parameters.
    */

    public CannyEdgeDetector() {
    lowThreshold = 2.5f;
    highThreshold = 7.5f;
    gaussianKernelRadius = 2f;
    gaussianKernelWidth = 16;
    contrastNormalized = false;
    }

    // accessors

    /**
    * The image that provides the luminance data used by this detector to
    * generate edges.
    *
    * @return the source image, or null
    */

    public BufferedImage getSourceImage() {
    return sourceImage;
    }

    /**
    * Specifies the image that will provide the luminance data in which edges
    * will be detected. A source image must be set before the process method
    * is called.
    *
    * @param image a source of luminance data
    */

    public void setSourceImage(BufferedImage image) {
    sourceImage = image;
    }

    /**
    * Obtains an image containing the edges detected during the last call to
    * the process method. The buffered image is an opaque image of type
    * BufferedImage.TYPE_INT_ARGB in which edge pixels are white and all other
    * pixels are black.
    *
    * @return an image containing the detected edges, or null if the process
    * method has not yet been called.
    */

    public BufferedImage getEdgesImage() {
    return edgesImage;
    }

    /**
    * Sets the edges image. Calling this method will not change the operation
    * of the edge detector in any way. It is intended to provide a means by
    * which the memory referenced by the detector object may be reduced.
    *
    * @param edgesImage expected (though not required) to be null
    */

    public void setEdgesImage(BufferedImage edgesImage) {
    this.edgesImage = edgesImage;
    }

    /**
    * The low threshold for hysteresis. The default value is 2.5.
    *
    * @return the low hysteresis threshold
    */

    public float getLowThreshold() {
    return lowThreshold;
    }

    /**
    * Sets the low threshold for hysteresis. Suitable values for this parameter
    * must be determined experimentally for each application. It is nonsensical
    * (though not prohibited) for this value to exceed the high threshold value.
    *
    * @param threshold a low hysteresis threshold
    */

    public void setLowThreshold(float threshold) {
    if (threshold < 0) throw new IllegalArgumentException();
    lowThreshold = threshold;
    }

    /**
    * The high threshold for hysteresis. The default value is 7.5.
    *
    * @return the high hysteresis threshold
    */

    public float getHighThreshold() {
    return highThreshold;
    }

    /**
    * Sets the high threshold for hysteresis. Suitable values for this
    * parameter must be determined experimentally for each application. It is
    * nonsensical (though not prohibited) for this value to be less than the
    * low threshold value.
    *
    * @param threshold a high hysteresis threshold
    */

    public void setHighThreshold(float threshold) {
    if (threshold < 0) throw new IllegalArgumentException();
    highThreshold = threshold;
    }

    /**
    * The number of pixels across which the Gaussian kernel is applied.
    * The default value is 16.
    *
    * @return the radius of the convolution operation in pixels
    */

    public int getGaussianKernelWidth() {
    return gaussianKernelWidth;
    }

    /**
    * The number of pixels across which the Gaussian kernel is applied.
    * This implementation will reduce the radius if the contribution of pixel
    * values is deemed negligable, so this is actually a maximum radius.
    *
    * @param gaussianKernelWidth a radius for the convolution operation in
    * pixels, at least 2.
    */

    public void setGaussianKernelWidth(int gaussianKernelWidth) {
    if (gaussianKernelWidth < 2) throw new IllegalArgumentException();
    this.gaussianKernelWidth = gaussianKernelWidth;
    }

    /**
    * The radius of the Gaussian convolution kernel used to smooth the source
    * image prior to gradient calculation. The default value is 16.
    *
    * @return the Gaussian kernel radius in pixels
    */

    public float getGaussianKernelRadius() {
    return gaussianKernelRadius;
    }

    /**
    * Sets the radius of the Gaussian convolution kernel used to smooth the
    * source image prior to gradient calculation.
    *
    * @return a Gaussian kernel radius in pixels, must exceed 0.1f.
    */

    public void setGaussianKernelRadius(float gaussianKernelRadius) {
    if (gaussianKernelRadius < 0.1f) throw new IllegalArgumentException();
    this.gaussianKernelRadius = gaussianKernelRadius;
    }

    /**
    * Whether the luminance data extracted from the source image is normalized
    * by linearizing its histogram prior to edge extraction. The default value
    * is false.
    *
    * @return whether the contrast is normalized
    */

    public boolean isContrastNormalized() {
    return contrastNormalized;
    }

    /**
    * Sets whether the contrast is normalized
    * @param contrastNormalized true if the contrast should be normalized,
    * false otherwise
    */

    public void setContrastNormalized(boolean contrastNormalized) {
    this.contrastNormalized = contrastNormalized;
    }

    // methods

    public void process() {
    width = sourceImage.getWidth();
    height = sourceImage.getHeight();
    picsize = width * height;
    initArrays();
    readLuminance();
    if (contrastNormalized) normalizeContrast();
    computeGradients(gaussianKernelRadius, gaussianKernelWidth);
    int low = Math.round(lowThreshold * MAGNITUDE_SCALE);
    int high = Math.round( highThreshold * MAGNITUDE_SCALE);
    performHysteresis(low, high);
    thresholdEdges();
    writeEdges(data);
    }

    // private utility methods

    private void initArrays() {
    if (data == null || picsize != data.length) {
    data = new int[picsize];
    magnitude = new int[picsize];

    xConv = new float[picsize];
    yConv = new float[picsize];
    xGradient = new float[picsize];
    yGradient = new float[picsize];
    }
    }

    //NOTE: The elements of the method below (specifically the technique for
    //non-maximal suppression and the technique for gradient computation)
    //are derived from an implementation posted in the following forum (with the
    //clear intent of others using the code):
    // http://forum.java.sun.com/thread.jspa?threadID=546211&start=45&tstart=0
    //My code effectively mimics the algorithm exhibited above.
    //Since I don't know the providence of the code that was posted it is a
    //possibility (though I think a very remote one) that this code violates
    //someone's intellectual property rights. If this concerns you feel free to
    //contact me for an alternative, though less efficient, implementation.

    private void computeGradients(float kernelRadius, int kernelWidth) {

    //generate the gaussian convolution masks
    float kernel[] = new float[kernelWidth];
    float diffKernel[] = new float[kernelWidth];
    int kwidth;
    for (kwidth = 0; kwidth < kernelWidth; kwidth++) {
    float g1 = gaussian(kwidth, kernelRadius);
    if (g1 <= GAUSSIAN_CUT_OFF && kwidth >= 2) break;
    float g2 = gaussian(kwidth - 0.5f, kernelRadius);
    float g3 = gaussian(kwidth + 0.5f, kernelRadius);
    kernel[kwidth] = (g1 + g2 + g3) / 3f / (2f * (float) Math.PI * kernelRadius * kernelRadius);
    diffKernel[kwidth] = g3 - g2;
    }

    int initX = kwidth - 1;
    int maxX = width - (kwidth - 1);
    int initY = width * (kwidth - 1);
    int maxY = width * (height - (kwidth - 1));

    //perform convolution in x and y directions
    for (int x = initX; x < maxX; x++) {
    for (int y = initY; y < maxY; y += width) {
    int index = x + y;
    float sumX = data[index] * kernel[0];
    float sumY = sumX;
    int xOffset = 1;
    int yOffset = width;
    for(; xOffset < kwidth ;) {
    sumY += kernel[xOffset] * (data[index - yOffset] + data[index + yOffset]);
    sumX += kernel[xOffset] * (data[index - xOffset] + data[index + xOffset]);
    yOffset += width;
    xOffset++;
    }

    yConv[index] = sumY;
    xConv[index] = sumX;
    }

    }

    for (int x = initX; x < maxX; x++) {
    for (int y = initY; y < maxY; y += width) {
    float sum = 0f;
    int index = x + y;
    for (int i = 1; i < kwidth; i++)
    sum += diffKernel[i] * (yConv[index - i] - yConv[index + i]);

    xGradient[index] = sum;
    }

    }

    for (int x = kwidth; x < width - kwidth; x++) {
    for (int y = initY; y < maxY; y += width) {
    float sum = 0.0f;
    int index = x + y;
    int yOffset = width;
    for (int i = 1; i < kwidth; i++) {
    sum += diffKernel[i] * (xConv[index - yOffset] - xConv[index + yOffset]);
    yOffset += width;
    }

    yGradient[index] = sum;
    }

    }

    initX = kwidth;
    maxX = width - kwidth;
    initY = width * kwidth;
    maxY = width * (height - kwidth);
    for (int x = initX; x < maxX; x++) {
    for (int y = initY; y < maxY; y += width) {
    int index = x + y;
    int indexN = index - width;
    int indexS = index + width;
    int indexW = index - 1;
    int indexE = index + 1;
    int indexNW = indexN - 1;
    int indexNE = indexN + 1;
    int indexSW = indexS - 1;
    int indexSE = indexS + 1;

    float xGrad = xGradient[index];
    float yGrad = yGradient[index];
    float gradMag = hypot(xGrad, yGrad);

    //perform non-maximal supression
    float nMag = hypot(xGradient[indexN], yGradient[indexN]);
    float sMag = hypot(xGradient[indexS], yGradient[indexS]);
    float wMag = hypot(xGradient[indexW], yGradient[indexW]);
    float eMag = hypot(xGradient[indexE], yGradient[indexE]);
    float neMag = hypot(xGradient[indexNE], yGradient[indexNE]);
    float seMag = hypot(xGradient[indexSE], yGradient[indexSE]);
    float swMag = hypot(xGradient[indexSW], yGradient[indexSW]);
    float nwMag = hypot(xGradient[indexNW], yGradient[indexNW]);
    float tmp;
    /*
    * An explanation of what's happening here, for those who want
    * to understand the source: This performs the "non-maximal
    * supression" phase of the Canny edge detection in which we
    * need to compare the gradient magnitude to that in the
    * direction of the gradient; only if the value is a local
    * maximum do we consider the point as an edge candidate.
    *
    * We need to break the comparison into a number of different
    * cases depending on the gradient direction so that the
    * appropriate values can be used. To avoid computing the
    * gradient direction, we use two simple comparisons: first we
    * check that the partial derivatives have the same sign (1)
    * and then we check which is larger (2). As a consequence, we
    * have reduced the problem to one of four identical cases that
    * each test the central gradient magnitude against the values at
    * two points with 'identical support'; what this means is that
    * the geometry required to accurately interpolate the magnitude
    * of gradient function at those points has an identical
    * geometry (upto right-angled-rotation/reflection).
    *
    * When comparing the central gradient to the two interpolated
    * values, we avoid performing any divisions by multiplying both
    * sides of each inequality by the greater of the two partial
    * derivatives. The common comparand is stored in a temporary
    * variable (3) and reused in the mirror case (4).
    *
    */
    if (xGrad * yGrad <= (float) 0 /*(1)*/
    ? Math.abs(xGrad) >= Math.abs(yGrad) /*(2)*/
    ? (tmp = Math.abs(xGrad * gradMag)) >= Math.abs(yGrad * neMag - (xGrad + yGrad) * eMag) /*(3)*/
    && tmp > Math.abs(yGrad * swMag - (xGrad + yGrad) * wMag) /*(4)*/
    : (tmp = Math.abs(yGrad * gradMag)) >= Math.abs(xGrad * neMag - (yGrad + xGrad) * nMag) /*(3)*/
    && tmp > Math.abs(xGrad * swMag - (yGrad + xGrad) * sMag) /*(4)*/
    : Math.abs(xGrad) >= Math.abs(yGrad) /*(2)*/
    ? (tmp = Math.abs(xGrad * gradMag)) >= Math.abs(yGrad * seMag + (xGrad - yGrad) * eMag) /*(3)*/
    && tmp > Math.abs(yGrad * nwMag + (xGrad - yGrad) * wMag) /*(4)*/
    : (tmp = Math.abs(yGrad * gradMag)) >= Math.abs(xGrad * seMag + (yGrad - xGrad) * sMag) /*(3)*/
    && tmp > Math.abs(xGrad * nwMag + (yGrad - xGrad) * nMag) /*(4)*/
    ) {
    magnitude[index] = gradMag >= MAGNITUDE_LIMIT ? MAGNITUDE_MAX : (int) (MAGNITUDE_SCALE * gradMag);
    //NOTE: The orientation of the edge is not employed by this
    //implementation. It is a simple matter to compute it at
    //this point as: Math.atan2(yGrad, xGrad);
    } else {
    magnitude[index] = 0;
    }
    }
    }
    }

    //NOTE: It is quite feasible to replace the implementation of this method
    //with one which only loosely approximates the hypot function. I've tested
    //simple approximations such as Math.abs(x) + Math.abs(y) and they work fine.
    private float hypot(float x, float y) {
    return (float) Math.hypot(x, y);
    }

    private float gaussian(float x, float sigma) {
    return (float) Math.exp(-(x * x) / (2f * sigma * sigma));
    }

    private void performHysteresis(int low, int high) {
    //NOTE: this implementation reuses the data array to store both
    //luminance data from the image, and edge intensity from the processing.
    //This is done for memory efficiency, other implementations may wish
    //to separate these functions.
    Arrays.fill(data, 0);

    int offset = 0;
    for (int y = 0; y < height; y++) {
    for (int x = 0; x < width; x++) {
    if (data[offset] == 0 && magnitude[offset] >= high) {
    follow(x, y, offset, low);
    }
    offset++;
    }
    }
    }

    private void follow(int x1, int y1, int i1, int threshold) {
    int x0 = x1 == 0 ? x1 : x1 - 1;
    int x2 = x1 == width - 1 ? x1 : x1 + 1;
    int y0 = y1 == 0 ? y1 : y1 - 1;
    int y2 = y1 == height -1 ? y1 : y1 + 1;

    data[i1] = magnitude[i1];
    for (int x = x0; x <= x2; x++) {
    for (int y = y0; y <= y2; y++) {
    int i2 = x + y * width;
    if ((y != y1 || x != x1)
    && data[i2] == 0
    && magnitude[i2] >= threshold) {
    follow(x, y, i2, threshold);
    return;
    }
    }
    }
    }

    private void thresholdEdges() {
    for (int i = 0; i < picsize; i++) {
    data[i] = data[i] > 0 ? -1 : 0xff000000;
    }
    }

    private int luminance(float r, float g, float b) {
    return Math.round(0.299f * r + 0.587f * g + 0.114f * b);
    }

    private void readLuminance() {
    int type = sourceImage.getType();
    if (type == BufferedImage.TYPE_INT_RGB || type == BufferedImage.TYPE_INT_ARGB) {
    int[] pixels = (int[]) sourceImage.getData().getDataElements(0, 0, width, height, null);
    for (int i = 0; i < picsize; i++) {
    int p = pixels[i];
    int r = (p & 0xff0000) >> 16;
    int g = (p & 0xff00) >> 8;
    int b = p & 0xff;
    data[i] = luminance(r, g, b);
    }
    } else if (type == BufferedImage.TYPE_BYTE_GRAY) {
    byte[] pixels = (byte[]) sourceImage.getData().getDataElements(0, 0, width, height, null);
    for (int i = 0; i < picsize; i++) {
    data[i] = (pixels[i] & 0xff);
    }
    } else if (type == BufferedImage.TYPE_USHORT_GRAY) {
    short[] pixels = (short[]) sourceImage.getData().getDataElements(0, 0, width, height, null);
    for (int i = 0; i < picsize; i++) {
    data[i] = (pixels[i] & 0xffff) / 256;
    }
    } else if (type == BufferedImage.TYPE_3BYTE_BGR) {
    byte[] pixels = (byte[]) sourceImage.getData().getDataElements(0, 0, width, height, null);
    int offset = 0;
    for (int i = 0; i < picsize; i++) {
    int b = pixels[offset++] & 0xff;
    int g = pixels[offset++] & 0xff;
    int r = pixels[offset++] & 0xff;
    data[i] = luminance(r, g, b);
    }
    } else {
    throw new IllegalArgumentException("Unsupported image type: " + type);
    }
    }

    private void normalizeContrast() {
    int[] histogram = new int[256];
    for (int i = 0; i < data.length; i++) {
    histogram[data[i]]++;
    }
    int[] remap = new int[256];
    int sum = 0;
    int j = 0;
    for (int i = 0; i < histogram.length; i++) {
    sum += histogram[i];
    int target = sum*255/picsize;
    for (int k = j+1; k <=target; k++) {
    remap[k] = i;
    }
    j = target;
    }

    for (int i = 0; i < data.length; i++) {
    data[i] = remap[data[i]];
    }
    }

    private void writeEdges(int pixels[]) {
    //NOTE: There is currently no mechanism for obtaining the edge data
    //in any other format other than an INT_ARGB type BufferedImage.
    //This may be easily remedied by providing alternative accessors.
    if (edgesImage == null) {
    edgesImage = new BufferedImage(width, height, BufferedImage.TYPE_INT_ARGB);
    }
    edgesImage.getWritableTile(0, 0).setDataElements(0, 0, width, height, pixels);
    }

    }
    41 changes: 41 additions & 0 deletions Main.java
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    package Lab9;

    import java.awt.BorderLayout;
    import java.awt.image.BufferedImage;
    import java.io.File;
    import java.io.IOException;

    import javax.imageio.ImageIO;
    import javax.swing.ImageIcon;
    import javax.swing.JFrame;
    import javax.swing.JLabel;
    import javax.swing.JPanel;

    public class Main {

    public static void main(String[] args) throws IOException {
    CannyEdgeDetector detector = new CannyEdgeDetector();

    //adjust its parameters as desired
    detector.setLowThreshold(0.5f);
    detector.setHighThreshold(1f);

    //apply it to an image
    BufferedImage img1 =ImageIO.read(new File("C:\\Users\\User\\Desktop\\Image\\Input\\Lenna.png"));
    detector.setSourceImage(img1);
    detector.process();
    BufferedImage edges = detector.getEdgesImage();
    JFrame frame = new JFrame();

    JLabel lblimage = new JLabel(new ImageIcon(edges));
    JPanel mainPanel = new JPanel(new BorderLayout());
    mainPanel.add(lblimage);
    // add more components here
    frame.add(mainPanel);
    frame.setVisible(true);

    frame.setSize(300, 400);

    }

    }